{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "959a247a",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#  Mplfinance Used To Plot Parabolic SAR\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6799029",
   "metadata": {},
   "source": [
    "### What is Parabolic SAR\n",
    "The parabolic stop and reverse, more commonly known as the \"Parabolic SAR,\" or \"PSAR\" is a trend-following indicator developed by J. Welles Wilder. It is displayed as a single parabolic line (or dots) underneath the price bars in an uptrend, and above the price bars in a downtrend.\n",
    "\n",
    "The parabolic SAR has three primary functions. First, it highlights the current price direction or trend. Second, it provides potential entry signals. Third, it provides potential exit signals."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc724a84",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### mplfinance 'yahoo' styles was used to customize:\n",
    "- Type of Plot Use `candle`\n",
    "- Parabolic SAR Build With Two Types Lines Named Upper Band and Lower Band\n",
    "- Background, Grid, and Figure Colors\n",
    "- Grid style\n",
    "- Y-Axis On The Right or Left\n",
    "- Matplotlib Defaults\n",
    "- Fill Between\n",
    "- Alpha\n",
    "- Color\n",
    "#### The simplest way to do this is to choose one of the `add_plot` that come packaged with `mplfinance`\n",
    "#### but, as we see below, it is also possible to customize your own `mplfinance styles`.\n",
    "#### Also Other Plot Type Can Be Used\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "466ece3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import mplfinance as mpf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "99d144e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This allows multiple outputs from a single jupyter notebook cell:\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9085a2ae",
   "metadata": {},
   "source": [
    "### Read in daily data for the S&P 500 from November of 2019 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "86a4ec31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 6)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-06-25</th>\n",
       "      <td>20.530001</td>\n",
       "      <td>20.83</td>\n",
       "      <td>19.99</td>\n",
       "      <td>20.040001</td>\n",
       "      <td>13.693501</td>\n",
       "      <td>61250600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-06-26</th>\n",
       "      <td>20.299999</td>\n",
       "      <td>20.76</td>\n",
       "      <td>20.15</td>\n",
       "      <td>20.629999</td>\n",
       "      <td>14.096654</td>\n",
       "      <td>52904900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open   High    Low      Close  Adj Close    Volume\n",
       "Date                                                               \n",
       "2003-06-25  20.530001  20.83  19.99  20.040001  13.693501  61250600\n",
       "2003-06-26  20.299999  20.76  20.15  20.629999  14.096654  52904900"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-04-07</th>\n",
       "      <td>28.08</td>\n",
       "      <td>28.129999</td>\n",
       "      <td>27.480000</td>\n",
       "      <td>27.620001</td>\n",
       "      <td>18.923342</td>\n",
       "      <td>72680200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-08</th>\n",
       "      <td>28.08</td>\n",
       "      <td>28.139999</td>\n",
       "      <td>27.200001</td>\n",
       "      <td>27.370001</td>\n",
       "      <td>18.752058</td>\n",
       "      <td>71791400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open       High        Low      Close  Adj Close    Volume\n",
       "Date                                                                   \n",
       "2004-04-07  28.08  28.129999  27.480000  27.620001  18.923342  72680200\n",
       "2004-04-08  28.08  28.139999  27.200001  27.370001  18.752058  71791400"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idf = pd.read_csv('../data/yahoofinance-INTC-19950101-20040412.csv',index_col=0,parse_dates=True).tail(200)\n",
    "\n",
    "df = idf.copy()\n",
    "df.index.name = 'Date'\n",
    "df.shape\n",
    "df.head(2)\n",
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa309397",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "The parabolic SAR (PSAR) indicator uses the most recent extreme price (EP) along with an acceleration factor (AF) to determine where the indicator dots will appear\n",
    "The parabolic SAR is calculated as follows:\n",
    "\n",
    "- Uptrend: PSAR = Prior PSAR + Prior AF (Prior EP - Prior PSAR)\n",
    "- Downtrend: PSAR = Prior PSAR - Prior AF (Prior PSAR - Prior EP)\n",
    "Where:\n",
    "\n",
    "- EP = Highest high for an uptrend and lowest low for a downtrend, updated each time a new EP is reached.\n",
    "- AF = Default of 0.02, increasing by 0.02 each time a new EP is reached, with a maximum of 0.20.\n",
    "\n",
    "- **Here is Following Calculation:**\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "842c2061",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Parabolic SAR Calculation With High and Low Values\n",
    "def calculate_psar(high, low, af_start=0.02, af_step=0.02, af_max=0.2):\n",
    "    psar = []\n",
    "    trend = []\n",
    "    af = af_start\n",
    "    af_direction = 1\n",
    "    extreme_point = low.iloc[0]\n",
    "    psar.append(extreme_point)\n",
    "    trend.append(-1)\n",
    "\n",
    "    for i in range(1, len(high)):\n",
    "        if trend[-1] == -1:\n",
    "            if high.iloc[i] > extreme_point:\n",
    "                extreme_point = high.iloc[i]\n",
    "                af = min(af + af_direction * af_step, af_max)\n",
    "            psar.append(psar[-1] + af * (extreme_point - psar[-1]))\n",
    "            if psar[-1] > low.iloc[i-1]:\n",
    "                psar[-1] = low.iloc[i-1]\n",
    "            if psar[-1] > low.iloc[i]:\n",
    "                trend.append(1)\n",
    "                extreme_point = low.iloc[i]\n",
    "                af = af_start\n",
    "            else:\n",
    "                trend.append(-1)\n",
    "        else:\n",
    "            if low.iloc[i] < extreme_point:\n",
    "                extreme_point = low.iloc[i]\n",
    "                af = min(af + af_direction * af_step, af_max)\n",
    "            psar.append(psar[-1] + af * (extreme_point - psar[-1]))\n",
    "            if psar[-1] < high.iloc[i-1]:\n",
    "                psar[-1] = high.iloc[i-1]\n",
    "            if psar[-1] < high.iloc[i]:\n",
    "                trend.append(-1)\n",
    "                extreme_point = high.iloc[i]\n",
    "                af = af_start\n",
    "            else:\n",
    "                trend.append(1)\n",
    "\n",
    "    return pd.Series(psar, index=high.index), pd.Series(trend, index=high.index)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a462e84c",
   "metadata": {},
   "source": [
    "### Segregation Positive and Negative Trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3acab0be",
   "metadata": {},
   "outputs": [],
   "source": [
    "def color(df):\n",
    "    UP = []\n",
    "    DOWN = []\n",
    "    for i in range(len(df)):\n",
    "        if df['PSAR'][i] < df['Close'][i]:\n",
    "            UP.append(float(df['PSAR'][i]))\n",
    "            DOWN.append(np.nan)\n",
    "        elif df['PSAR'][i] > df['Close'][i]:\n",
    "            DOWN.append(float(df['PSAR'][i]))\n",
    "            UP.append(np.nan)\n",
    "        else:\n",
    "            UP.append(np.nan)\n",
    "            DOWN.append(np.nan)\n",
    "    df['up'] = UP\n",
    "    df['down'] = DOWN\n",
    "    return df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c9840c42",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Getting High's And Low's From df\n",
    "high = df['High']\n",
    "low = df['Low']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "745c4e74",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function For Calculation Return df\n",
    "df['PSAR'], df['PSAR_1'] = calculate_psar(high, low, af_start=0.02, af_step=0.02, af_max=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9236db97",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Color Seperation\n",
    "psar_df = color(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d6daf32a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Extracted And New Variable Applied\n",
    "a = psar_df[['up']]\n",
    "b = psar_df[['down']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f52eddd7",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Let's say we want to plot the Ichimoku Cloud along with the basic OHLCV plot.  \n",
    "\n",
    "We Use `make_addplot()` to create the addplot dict, and pass that into the plot() function:\n",
    "\n",
    "We Use `Color` To Define Line Colors\n",
    "\n",
    "We Use `alpha` To Define Depth Line Color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7bf67a45",
   "metadata": {},
   "outputs": [],
   "source": [
    "psar = [\n",
    "    mpf.make_addplot(a,scatter=True,color='green',),\n",
    "    mpf.make_addplot(b,scatter=True,color='red',),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "af4af23b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpf.plot(\n",
    "    df,\n",
    "    volume=True,\n",
    "    type=\"candle\",\n",
    "    style=\"yahoo\",\n",
    "    addplot=psar,\n",
    "    figsize=(20,10),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba2c3f20",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
